Considerations on the elements of quantifying human reliability

Sträter, Oliver

doi:10.1016/j.ress.2003.09.015

Cited by 23 publications

(8 citation statements)

References 3 publications

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“…Current HRA practice implies sometimes arbitrary quantitative evaluation of Human Error Probability due to the data validation problem [6,7]. Indeed, the quantification lies in tables of human error probabilities or on probability distributions.…”

Section: Human Reliability Analysismentioning

confidence: 99%

Dispatcher reliability analysis: SPICA-RAIL experiments

Belmonte

Boulanger

Schön

2009

Safety and Security Engineering III

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Although considered as not safety critical (safety function being performed by other sub-systems), railway supervision systems can contribute to hazardous scenarios. It is of primary importance to identify this type of scenario and evaluate the behaviour of human operators. A state of the art in human reliability is presented. This article presents an experimental protocol based on an automatic train supervision system coupled to a traffic simulator. It allows one to gather data for human reliability evaluation and man-machine performance studies.

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Section: Human Reliability Analysismentioning

confidence: 99%

Dispatcher reliability analysis: SPICA-RAIL experiments

Belmonte

Boulanger

Schön

2009

Safety and Security Engineering III

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“…The limitations of existing HRA methods have been widely discussed previously (Woods, 1990, Hollnagel, 2000, Mosleh and Chang, 2004, Sträter, 2004, Boring et al, 2007, French et al, 2011, Groth and Swiler, 2013. Two interrelated shortcomings in existing HRA methods are the limited scientific basis used to develop those methods and the use of simplified modeling techniques, which lack causal structure and quantitative traceability.…”

Section: Introductionmentioning

confidence: 99%

Capturing cognitive causal paths in human reliability analysis with Bayesian network models

Zwirglmaier

Straub

Groth

2017

Reliability Engineering & System Safety

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In the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC (Xing et al., 2013). We illustrate how the model could be quantified with a combination of expertprobabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA.

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“…In this article, the term "data" refers to both quantitative data (e.g., failure rates), which are needed for human reliability quantification, and qualitative data (e.g., descriptions of operation circumstances), which are needed for human error probability (HEP) modification and search for error prone situations. In terms of the relative portion of qualitative and quantitative information contained in the data, human performance data differentiate on a hierarchy of four data levels: nominal scale, ordinal scale, relative scale, and absolute scale [6]. Data at the absolute scale level refers to numerical probabilities values (i.e., quantitative information) that can be directly used in HRAs, while data at the nominal scale refers to general descriptions concerning certain aspects of human behavior (i.e., qualitative information).…”

Section: Introductionmentioning

confidence: 99%

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Liao

Groth

Marie

2015

Reliability Engineering & System Safety

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This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

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Considerations on the elements of quantifying human reliability

Cited by 23 publications

References 3 publications

Dispatcher reliability analysis: SPICA-RAIL experiments

Dispatcher reliability analysis: SPICA-RAIL experiments

Capturing cognitive causal paths in human reliability analysis with Bayesian network models

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

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